Lysine crotonylation (Kcr) is a crucial post-translational modification that significantly influences various biological processes in both animals and plants, including gene transcription, cellular metabolism, and photosynthesis. Accurately identifying Kcr sites is essential for advancing research in these areas. However, traditional experimental methods often face limitations that make this identification challenging. To bridge this gap, we are excited to introduce our cutting-edge web tool designed to predict Kcr sites using an advanced deep learning model. This tool integrates traditional protein coding techniques with deep learning for effective feature extraction. Key Features: Innovative Deep Learning Model: Our model leverages the power of deep learning to enhance the accuracy of Kcr site predictions. Feature Fusion: By combining various features, we achieve superior classification performance compared to existing state-of-the-art models. Detailed Interpretability Analysis: Our tool not only provides predictions but also offers insights into the underlying mechanisms, enhancing the understanding of Kcr site identification. This user-friendly tool is designed for researchers and scientists looking to efficiently identify lysine crotonylation sites, facilitating advancements in the study of biological processes in plants and animals.